Abstract

Specific types of multi-net neural computing systems can give improved generalisation performance over single network solutions. In single-net systems learning is one way in which good generalisation can be achieved, where a number of neurons are combined through a process of collaboration. In this thesis we examine collaboration in multi-net systems through in-situ learning. Here we explore how generalisation can be improved through learning in the components and their combination at the same time. To achieve this we present a formal way in which multi-net systems can be described in an attempt to provide a method with which the general properties of multi-net systems can be explored. We then explore two novel learning algorithms for multi-net systems that exploit in-situ learning, evaluating them in comparison with multi-net and single-net solutions. Last, we simulate two cognitive processes with in-situ learning to examine the interaction between different numerical abilities in multi-net systems. Using single-net simulations of subitization and counting we build a multi-net simulation of quantification. Similarly, we combine single-net simulations of the fact retrieval and 'count all' addition strategies into a multi-net simulation of addition. Our results are encouraging, with improved generalisation performance obtained on benchmark problems, and the interaction of strategies with in-situ learning used to describe well known numerical ability phenomena. This learning through interaction in connectionist simulations we call integrated learning.